Automated Processing for Flood Area Detection Using ALOS-2 and Hydrodynamic Simulation Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 17; p. 2709
• Main Authors: Ohki, Masato, Yamamoto, Kosuke, Tadono, Takeo, Yoshimura, Kei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2020
• Subjects: Automation; Bayesian analysis; Climate change; Computation; Computer simulation; Datasets; Disaster management; disaster monitoring; Disasters; Feasibility studies; Flood mapping; flood model; Floods; Hydrologic data; Machine learning; Mapping; Monitoring; PALSAR-2; radar interferometry; Remote sensing; Satellite observation; Satellites; Simulation; Statistical inference; Synthetic aperture radar; Typhoons; Vegetation; Japan
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12172709

Rapid and frequent mapping of flood areas are essential for monitoring and mitigating flood disasters. The Advanced Land Observing Satellite-2 (ALOS-2) carries an L-band synthetic aperture radar (SAR) capable of rapid and frequent disaster observations. In this study, we developed a fully automatic, fast computation, and robust method for detecting flood areas using ALOS-2 and hydrodynamic flood simulation data. This study is the first attempt to combine flood simulation data from the Today’s Earth system (TE) with SAR-based disaster mapping. We used Bayesian inference to combine the amplitude/coherence data by ALOS-2 and the flood fraction data by TE. Our experimental results used 12 flood observation sets of data from Japan and had high accuracy and robustness for use under various ALOS-2 observation conditions. Flood simulation contributed to improving the accuracy of flood detection and reducing computation time. Based on these findings, we also assessed the operability of our method and found that the combination of ALOS-2 and TE data with our analysis method was capable of daily flood monitoring.